Indeed, the first attempts to perform decoding operations with “raw” neural networks (i.e. without
using the underlying graph structures of existing sub-optimal algorithms, as done in [1]}) were unsuccessful.
For instance, an exponential number of neurons in the network is needed in [2]} to achieve satisfactory performance when decoding small length polar codes.
We made the same observation when we tried to decode dense lattices typically used for channel coding [3]}.
So far, it was not clear whether such a behavior is due to either an unadapted learning algorithm or a consequence of the complexity of the function to learn.
However, unlike for channel decoding (i.e. dense lattice decoding), neural networks can sometimes be successfully trained in the scope of multiple-input multiple-output (MIMO) detection [4]}[3]}.
Note that it is also possible to unfold existing iterative algorithms to establish the neural network structure for MIMO detection as done in [6]}. For lattices in reasonable number of dimensions it is possible to maintain sphere decoding but tune its parameters via a neural network [7]}, this is outside the context of our study.
